Machine learning (ML) systems are composed of (usually large) numbers of adaptive weights. The goal of ML is to adapt the values of these weights based on exposure to data to optimize a function, for example, temporal prediction, spatial classification, or reward. The foundation objective of ML creates friction with modern methods of computing, since every adaptation event necessarily reduces to a communication procedure between memory and processing resources separated by a distance. The power required to simulate the adaptive network grows impractically large, owing to the tremendous energy consumed shuttling information back and forth.
Nature, on the other hand, does not separate memory and processing. Rather, the act of memory access is the act of computing is the act of adaptation. The memory processing distance goes to zero and power efficiency explodes by factors exceeding a billion.
Modern computing allows us to explore the universe of all possible ways to adapt. Creating intrinsically adaptive hardware implies that we give up this flexibility and rely on just one method. After all, neurobiological researchers have unearthed dozens of plasticity methods in a brain, which would seem to imply that they are all important in some way or another. If we take a step back and look at all of Nature, however, we find that a viable solution is literally all around us in both biological and non-biological systems. The solution is remarkably simple and it is obviously universal.
We find the solution around us in rivers, lightning, and trees, but also deep within us. The air that we breathe is coupled to our blood through thousands of bifurcating channels that form our lungs. Our brain is coupled to our blood through thousands of bifurcating channels that form our circulatory system, and our neurons are coupled to our brain through the thousands of bifurcating channels forming our axons and dendrites. At all scales we see flow systems built of a very simple fractal building block.